Amazon AWS Certified Generative AI Developer

Question 6 of 97

A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarities to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must be able to search documents that are in English, Spanish, and Portuguese. The application must filter documents by metadata such as publication date, regulatory agency, and document type.

The database stores approximately 10 million document embeddings. To minimize operational overhead, the company wants a solution that minimizes management and maintenance effort. The application must provide low-latency responses for real-time customer interactions.

Which solution will meet these requirements?

Use Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering. Connect to Amazon Bedrock Knowledge Bases to enable Retrieval Augmented Generation (RAG) capabilities that use an Anthropic Claude foundation model (FM).

Deploy an Amazon Aurora PostgreSQL database with the pgvector extension. Define tables to store embeddings and metadata. Use SQL queries to perform similarity searches. Send retrieved documents to Amazon Bedrock to generate responses.

Use Amazon S3 Vectors to configure a vector index and non-filterable metadata fields. Integrate S3 Vectors with Amazon Bedrock to enable Retrieval Augmented Generation (RAG) capabilities.

Set up an Amazon Neptune Analytics graph database. Configure a vector index that has appropriate dimensionality to store document embeddings. Use Amazon Bedrock to perform graph-based retrieval and to generate responses.

Question 7 of 97

A medical company is building a generative AI (GenAI) application that uses RAG to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end user latency, even as the document collection grows to millions of documents.

Which solution will meet these requirements with the LEAST operational overhead?

Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.

Increase the dimensions of the vector embeddings from 384 to 1536. Use a post-processing AWS Lambda function to filter out irrelevant results after retrieval.

Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.

Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model that is hosted on Amazon SageMaker AI.

Question 8 of 97

A company runs a generative AI (GenAI)-powered summarization application in an application AWS account that uses Amazon Bedrock. The application architecture includes an Amazon API Gateway REST API that forwards requests to AWS Lambda functions that are attached to private VPC subnets. The application summarizes sensitive customer records that the company stores in a governed data lake in a centralized data storage account. The company has enabled Amazon S3, Amazon Athena, and AWS Glue in the data storage account.

The company must ensure that calls that the application makes to Amazon Bedrock use only private connectivity between the company's application VPC and Amazon Bedrock. The company's data lake must provide fine-grained column-level access across the company's AWS accounts.

Which solution will meet these requirements?

In the application account, create interface VPC endpoints for Amazon Bedrock runtimes. Run Lambda functions in private subnets. Use IAM conditions on inference and data-plane policies to allow calls only to approved endpoints and roles. In the data storage account, use AWS Lake Formation LF-tag-based access control to create table and column-level cross-account grants.

Run Lambda functions in private subnets. Configure a NAT gateway to provide access to Amazon Bedrock and the data lake. Use S3 bucket policies and ACLs to manage permissions. Export AWS CloudTrail logs to Amazon S3 to perform weekly reviews.

Create a gateway endpoint only for Amazon S3 in the application account. Invoke Amazon Bedrock through public endpoints. Use database-level grants in AWS Lake Formation to manage data access. Stream AWS CloudTrail logs to Amazon CloudWatch Logs. Do not set up metric filters or alarms.

Use VPC endpoints to provide access to Amazon Bedrock and Amazon S3 in the application account. Use only IAM path-based policies to manage data lake access. Send AWS CloudTrail logs to Amazon CloudWatch Logs. Periodically create dashboards and allow public fallback for cross-Region reads to reduce setup time.

Question 9 of 97

A media company must use Amazon Bedrock to implement a robust governance process for AI-generated content. The company needs to manage hundreds of prompt templates. Multiple teams use the templates across multiple AWS Regions to generate content. The solution must provide version control with approval workflows that include notifications for pending reviews. The solution must also provide detailed audit trails that document prompt activities and consistent prompt parameterization to enforce quality standards.

Which solution will meet these requirements?

Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch to create dashboards that display prompt usage metrics. Store the approval status of content in Amazon DynamoDB. Use AWS Lambda functions to enforce approvals.

Use Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use IAM policies to control approval permissions. Create parameterized prompt templates by specifying variables.

Use AWS Step Functions to create an approval workflow. Store prompts as documents in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.

Deploy Amazon SageMaker Canvas with prompt templates that are stored in Amazon S3. Use AWS CloudFormation to implement version control. Use AWS Config to enforce approval policies.

Question 10 of 97

A company is developing a customer support application that uses Amazon Bedrock foundation models (FMs) to provide real-time AI assistance to the company's employees. The application must display AI-generated responses character by character as the responses are generated. The application needs to support thousands of concurrent users with minimal latency. The responses typically take 15 to 45 seconds to finish.

Which solution will meet these requirements?

Configure an Amazon API Gateway WebSocket API with an AWS Lambda integration. Configure the WebSocket API to invoke the Amazon Bedrock InvokeModelWithResponseStream API and stream partial responses through WebSocket connections.

Configure an Amazon API Gateway REST API with an AWS Lambda integration. Configure the REST API to invoke the Amazon Bedrock standard InvokeModel API and implement frontend client-side polling every 100 ms for complete response chunks.

Implement direct frontend client connections to Amazon Bedrock by using IAM user credentials and the InvokeModelWithResponseStream API without any intermediate gateway or proxy layer.

Configure an Amazon API Gateway HTTP API with an AWS Lambda integration. Configure the HTTP API to cache complete responses in an Amazon DynamoDB table and serve the responses through multiple paginated GET requests to frontend clients.